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--- |
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license: mit |
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tags: |
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- spatial-transcriptomics |
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- graph-transformer |
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- gene-expression |
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- pretrained |
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- pytorch |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: feature-extraction |
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--- |
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# SpatialGT Pretrained Model |
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## Model Description |
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This is the **pretrained checkpoint** of SpatialGT (Spatial Graph Transformer), a graph transformer model designed for spatial transcriptomics data analysis. |
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SpatialGT leverages spatial context through neighbor-aware attention mechanisms for: |
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- 🗺️ Spatial context learning from large-scale spatial transcriptomics data |
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- 🧬 Gene expression reconstruction |
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- 🔬 Perturbation simulation |
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## Model Details |
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- **Architecture**: Graph Transformer with spatial neighbor attention |
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- **Parameters**: ~600M |
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- **Training Data**: Large-scale spatial transcriptomics atlas |
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- **Input**: Gene expression vectors with spatial coordinates |
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- **Output**: Contextualized gene expression representations |
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## Usage |
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```python |
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import torch |
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from pretrain.model_spatialpt import SpatialNeighborTransformer |
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from pretrain.Config import Config |
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# Load configuration |
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config = Config() |
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# Initialize model |
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model = SpatialNeighborTransformer(config) |
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# Load pretrained weights |
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from safetensors.torch import load_file |
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state_dict = load_file("model.safetensors") |
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model.load_state_dict(state_dict) |
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model.eval() |
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``` |
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## Files |
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- `model.safetensors`: Model weights in safetensors format |
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- `training_args.bin`: Training arguments |
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- `trainer_state.json`: Training state information |
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## Citation |
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If you use this model, please cite our paper (details to be added upon publication). |
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## License |
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MIT License |
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